The Latest Trends in Attention Mechanisms and Their Application in Medical Imaging

Autor: Hyungseob Shin, Jeongryong Lee, Taejoon Eo, Yohan Jun, Sewon Kim, Dosik Hwang

Publikováno v: 대한영상의학회지, Vol 81, Iss 6, Pp 1305-1333 (2020)

Deep learning has recently achieved remarkable results in the field of medical imaging. However, as a deep learning network becomes deeper to improve its performance, it becomes more difficult to interpret the processes within. This can especially

Externí odkaz: https://doaj.org/article/2f75bb5f61d54e529c97889bf6a31d6d

Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning

Autor: Yohan Jun, Yae Won Park, Hyungseob Shin, Yejee Shin, Jeong Ryong Lee, Kyunghwa Han, Sung Soo Ahn, Soo Mee Lim, Dosik Hwang, Seung-Koo Lee

Publikováno v: European Radiology.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::abaea17852feb6ba34911fd4400aa5de
https://doi.org/10.1007/s00330-023-09590-4

Joint Deep Model-based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI

Autor: Dosik Hwang, Taejoon Eo, Hyungseob Shin, Yohan Jun

Publikováno v: CVPR

Magnetic resonance imaging (MRI) can provide diagnostic information with high-resolution and high-contrast images. However, MRI requires a relatively long scan time compared to other medical imaging techniques, where long scan time might occur patien

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7f02f206e678ba24a0d77f3dbf106b43
https://doi.org/10.1109/cvpr46437.2021.00523

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

Autor: Yohan Jun, Simon Arberet, Jean-Luc Starck, Dominik Nickel, Alireza Radmanesh, Yvonne W. Lui, Sunwoo Kim, Matthew J. Muckley, Mahmoud Mostapha, Jonas Teuwen, Zhengnan Huang, Nafissa Yakubova, Dosik Hwang, Geunu Jeong, Zaccharie Ramzi, Florian Knoll, Anuroop Sriram, Philippe Ciuciu, Chaoping Zhang, Bruno Riemenschneider, Hyungseob Shin, Jingyu Ko, Dimitrios Karkalousos

Publikováno v: IEEE Transactions on Medical Imaging
IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, In press, ⟨10.1109/TMI.2021.3075856⟩
IEEE transactions on medical imaging
IEEE Transactions on Medical Imaging, In press, ⟨10.1109/TMI.2021.3075856⟩

Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided partici

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ada6cd85033aac2da6c4388d81c59f5a
https://hal.archives-ouvertes.fr/hal-03066150v2/document

Accelerating Cartesian MRI by domain-transform manifold learning in phase-encoding direction

Autor: Hyungseob Shin, Tae-Seong Kim, Taejoon Eo, Dosik Hwang, Yohan Jun

Publikováno v: Medical image analysis. 63

This study developed a domain-transform framework comprising domain-transform manifold learning with an initial analytic transform to accelerate Cartesian magnetic resonance imaging (DOTA-MRI). The proposed method directly transforms undersampled Car

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f24680079f9c4080c91e796a925abbd8
https://pubmed.ncbi.nlm.nih.gov/32299061